Safety monitoring method and device based on preposed risk prediction, equipment, medium and program product

Abstract

The embodiment of the invention provides a safety monitoring method and device based on preposed risk prediction, equipment, a medium and a program product, and relates to the technical field of vehicle safety monitoring, and the method comprises the steps: obtaining vehicle state information; according to the vehicle state information, judging whether a pre-risk prediction result triggers a set condition or not; if the set condition is triggered, starting an alarm, recording burying point data, and uploading the burying point data to a cloud end to instruct the cloud end to carry out risk analysis; receiving and executing a security risk repair instruction of the cloud to complete security monitoring; the method has a prediction effect on possible risks existing in the automatic driving process, detects existing potential safety hazards, and reduces the probability of occurrence of safety accidents.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle safety monitoring, and in particular to a safety monitoring method, device, equipment, medium and program product based on pre-risk prediction. Background Art

[0002] The trigger conditions for traditional autonomous driving systems to collect data are when the system processing capacity is insufficient and the driver takes over, or when a safety condition (such as emergency braking) is triggered. This traditional data collection trigger covers limited scenarios, and the collected driving data is limited to a few specific situations. It cannot predict the possible risks in the autonomous driving process, and it cannot quickly accumulate vehicle-side data to support subsequent analysis and technical improvements. It cannot detect existing safety hazards, leading to safety accidents. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a safety monitoring method, device, equipment, medium and program product based on pre-risk prediction, so as to solve the problem that the existing vehicle monitoring technology cannot predict the possible risks in the automatic driving process and cannot detect the existing safety hazards, leading to the occurrence of safety accidents.

[0004] In a first aspect, an embodiment of the present application provides a security monitoring method based on pre-risk prediction, comprising:

[0005] Get vehicle status information;

[0006] According to the vehicle status information, determining whether the result of the pre-risk prediction triggers a set condition;

[0007] If the set conditions are triggered, the alarm is activated, the tracking data is recorded, and the tracking data is uploaded to the cloud to instruct the cloud to perform risk analysis;

[0008] Receive and execute security risk remediation instructions from the cloud to complete security monitoring.

[0009] In the above implementation process, the embodiment of the present application obtains vehicle status information; based on the vehicle status information, determines whether the result of the pre-risk prediction triggers the set conditions; if the set conditions are triggered, activates the alarm, records the buried data, and uploads the buried data to the cloud to instruct the cloud to perform risk analysis; receives and executes security risk repair instructions from the cloud to complete security monitoring; predicts possible risks in the autonomous driving process, detects existing safety hazards, and reduces the probability of safety accidents.

[0010] Furthermore, the vehicle status information includes: radar monitoring signals, various function status signals, various system fault signals, driver status safety signals, and four-door and two-hood status signals.

[0011] In the above implementation process, by collecting and analyzing vehicle status data such as radar monitoring signals, various functional status signals, various system fault signals, driver status safety signals, four-door and two-lid status signals, etc., it is possible to cover richer scenarios, collect real vehicle data more efficiently and in a more targeted manner, accumulate richer data on potential functional deficiencies of autonomous driving, and complete the pre-risk prediction in autonomous driving.

[0012] Furthermore, the determining whether the result of the pre-risk prediction triggers a set condition includes:

[0013] Determine whether the results of the pre-risk prediction trigger the set conditions, where the scenario categories include dangerous operation scenarios, abnormal vehicle operation, vehicle failure, driver misuse, and safety warnings.

[0014] In the above implementation process, it is determined whether the result of the pre-risk prediction triggers the set conditions, and the pre-risk prediction in autonomous driving is completed; five pre-risk prediction categories are defined, which can achieve more efficient and targeted collection of real vehicle data, and realize richer data accumulation for potential functional deficiencies of autonomous driving.

[0015] Furthermore, the determining whether the result of the pre-risk prediction triggers a set condition includes:

[0016] In the dangerous operation scenario category, the ESP vehicle stability control system, ABS anti-lock braking system and four-door and two-lid signal analysis are used to determine whether the vehicle is in a dangerous scenario;

[0017] Among the types of vehicle operation anomalies, determine whether the autonomous driving vehicle is abnormal by monitoring radar signals and autonomous driving status signals;

[0018] Among the types of vehicle faults, it is determined whether the vehicle has a fault by monitoring the fault signals of the steering and braking systems;

[0019] In the driver misuse category, the driver is judged whether there is dangerous or misuse driving behavior by monitoring the DMS signal; wherein the DMS signal includes the seat buckle signal and the fatigue warning signal;

[0020] In the safety warning category, by monitoring the front collision warning FCW activation signal and the lane departure warning LDW activation signal, it is predicted whether the vehicle has a collision safety risk.

[0021] In the above implementation process, five pre-risk prediction categories were defined, which can achieve more efficient and targeted collection of real vehicle data, and achieve richer data accumulation for potential functional deficiencies of autonomous driving.

[0022] Furthermore, if the set condition is triggered, the alarm is activated, including:

[0023] When there are abnormalities in the monitoring signals corresponding to different scene categories, it is regarded as the triggering of the corresponding rules and the alarm is activated.

[0024] In the above implementation process, when conducting pre-risk prediction, once a potential safety hazard is found, an alarm is quickly given to the driver to enhance safety.

[0025] Furthermore, the recording of the tracking data and uploading the tracking data to the cloud to instruct the cloud to perform risk analysis include:

[0026] Record buried data;

[0027] The vehicle-side trigger sends the data collection request to the big data collection component to trigger the upload of the vehicle's tracking data, instruct the cloud to complete scene restoration, problem analysis and risk assessment based on the tracking data, and send instructions for security risk repair and software upgrade;

[0028] Among them, the buried data includes: basic vehicle information, vehicle status and dynamic information, driver status and operation information, perception-related information, automatic driving system operation information, instrument and HMI display information.

[0029] In the above implementation process, through the data recording and uploading functions, scene data is accumulated, R&D technical analysis is supported, long-tail problem vulnerabilities are solved, and product security is improved.

[0030] In a second aspect, an embodiment of the present application provides a security monitoring device based on pre-risk prediction, comprising:

[0031] An information acquisition module, used to acquire vehicle status information;

[0032] A risk prediction module, used to determine whether the result of the pre-risk prediction triggers a set condition according to the vehicle status information;

[0033] The alarm feedback module is used to activate the alarm if the set conditions are triggered, record the buried point data, and upload the buried point data to the cloud to instruct the cloud to perform risk analysis;

[0034] The risk repair module is used to receive and execute security risk repair instructions from the cloud to complete security monitoring.

[0035] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0036] A processor, a memory and a bus, wherein the processor is connected to the memory via the bus, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, they are used to implement the security monitoring method based on pre-risk prediction as described above.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a server, the security monitoring method based on pre-risk prediction as described above is implemented.

[0038] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes instructions, and when the instructions are executed by a computer, the computer implements the security monitoring method based on pre-risk prediction as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of a process flow of a security monitoring method based on pre-risk prediction provided in an embodiment of the present application;

[0041] Figure 2 It is a structural schematic diagram of a security monitoring device based on pre-risk prediction provided by an embodiment of the present application;

[0042] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0044] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0045] OBD (On-Board Diagnostics) is a system used to monitor and control vehicle emissions and engine performance.

[0046] ESP (Electronic Stability Program) is a system that can analyze emergency driving conditions and correct driving errors. It is a type of active safety technology.

[0047] ABS (Antilock Brake System) is an advanced automobile safety system that prevents the wheels from locking during emergency braking, thereby maintaining the vehicle's steering ability and stability, reducing braking distance, and reducing the risk of accidents.

[0048] NOP (Navigate on Pilot) is an advanced driver assistance system that combines high-precision maps, positioning technology, sensor data and advanced algorithms to enable autonomous navigation and driving of the vehicle.

[0049] AVP (Autonomous Valet Parking) is an advanced autonomous driving technology that allows a vehicle to automatically complete the parking process without a driver.

[0050] FCW (Forward Collision Warning) is an advanced driver assistance system that uses sensors such as radar, camera or lidar to monitor the road conditions in front of the vehicle.

[0051] DMS (Driver Monitoring System) is a technical system used to monitor the driver's driving status.

[0052] The LDW (Lane Departure Warning) system is an advanced driving assistance technology designed to help the driver keep the vehicle in the correct lane and reduce traffic accidents caused by lane departure.

[0053] VIN (Vehicle Identification Number) is a unique 17-digit code that uniquely identifies each car. This code contains information such as the vehicle's manufacturer, year, model, engine type, production plant, and production serial number.

[0054] HMI (Human-Machine Interface) is a device and system used for interaction between humans and machines. It allows users to communicate and operate machines through graphical interfaces, touch screens, buttons, sounds, etc.

[0055] DTC (Diagnostic Trouble Code) is a code used to indicate a specific fault or problem in a car's electronic control system.

[0056] OTA (Over-The-Air) is a wireless update technology that allows devices (such as smartphones, tablets, car systems, etc.) to receive and install software updates from a remote server via a wireless network.

[0057] Please see Figure 1 , Figure 1 A schematic diagram of a safety monitoring method based on pre-risk prediction provided in an embodiment of the present application. Figure 1 The safety monitoring method based on pre-risk prediction includes:

[0058] 100. Obtain vehicle status information.

[0059] Specifically, the vehicle status information includes: radar monitoring signals, various function status signals, various system fault signals, driver status safety signals, and four-door and two-lid status signals.

[0060] It can be understood that the vehicle status data is collected through the vehicle's sensors and interfaces.

[0061] Among them, radar monitoring signals are obtained through vehicle-mounted radar sensors to provide real-time environmental data. Vehicle-mounted radar sensors monitor the vehicle's surrounding environment by transmitting and receiving radio waves. Radio waves can be used to detect obstacles, vehicles, pedestrians, etc., and measure their distance and speed from the vehicle.

[0062] Among them, the functional status signal can be obtained through the vehicle's on-board diagnostic system (OBD) interface, which can be connected to professional diagnostic tools or displayed through the vehicle information display system; the functional status signal includes the working status of key components such as the vehicle's engine status, fuel system, and transmission system.

[0063] Among them, the system fault signal refers to the signal generated when a fault occurs in each system in the vehicle. It can be obtained through the OBD system, trigger the vehicle's fault indicator light, and provide a fault code to help diagnose the problem.

[0064] Among them, the driver status safety signal can be obtained through the driver monitoring system (DMS), specifically through cameras, biosensors, etc., for real-time monitoring of the driver's facial expression, eye state, head posture, etc. The driver status safety signal involves the driver's physiological and behavioral state, such as fatigue, distraction, etc.

[0065] Among them, the four-door and two-lid status signal is usually provided by the door and lid switch sensors, which send the status information to the vehicle's control module and report it to the driver through the vehicle's information display system. The four-door and two-lid status signal refers to the open and closed status of the vehicle's four doors and two trunk lids (hood and tailgate).

[0066] Therefore, by collecting and analyzing vehicle status data such as radar monitoring signals, various functional status signals, various system fault signals, driver status safety signals, four-door and two-lid status signals, etc., we can cover richer scenarios, collect real vehicle data more efficiently and in a more targeted manner, accumulate richer data on potential functional deficiencies of autonomous driving, and complete the pre-risk prediction in autonomous driving.

[0067] 200. Determine, based on the vehicle status information, whether a result of the pre-risk prediction triggers a set condition.

[0068] Specifically, determine whether the result of the pre-risk prediction triggers the set conditions, where the scenario categories include dangerous operation scenarios, vehicle operation abnormalities, vehicle failures, driver misuse, and safety warnings; where, determine whether the result of the pre-risk prediction triggers the set conditions to complete the pre-risk prediction in autonomous driving; define five pre-risk prediction categories, which can achieve more efficient and targeted collection of real vehicle data, and achieve richer data accumulation for potential functional deficiencies of autonomous driving.

[0069] Optionally, in the dangerous operation scenario category, the ESP vehicle stability control system, ABS anti-lock braking system and four-door and two-lid signal analysis are monitored to determine whether the vehicle is in a dangerous scenario. Exemplarily, in the pre-risk prediction category of dangerous operation scenarios, under automatic driving (NOP+AVP), if ESP is activated, the body stability system is triggered.

[0070] Optionally, in the category of abnormal vehicle operation, the radar signal and the automatic driving state signal are monitored to determine whether the automatic driving vehicle is abnormal. Exemplarily, in the category of pre-risk prediction of abnormal vehicle operation, if the steering system reports a serious fault, a vehicle fault is triggered.

[0071] Optionally, in the vehicle fault category, the steering and braking system fault signals are monitored to determine whether the vehicle has a fault. Exemplarily, in the pre-risk prediction category of vehicle fault, in the case of automatic driving and non-automatic driving, if the new energy battery thermal runaway is quoted, the battery protection type risk is triggered.

[0072] Optionally, in the driver misuse category, the DMS signal is monitored to determine whether the driver has dangerous or misuse driving behavior; wherein the DMS signal includes a seat buckle signal and a fatigue alarm signal. Exemplarily, in the driver misuse pre-risk prediction category, under the NOP function, if the driver leaves the seat for more than 1s, the system triggers the passenger wake-up protection mechanism.

[0073] Optionally, in the safety warning category, the vehicle is predicted to have a collision safety risk by monitoring the activation signal of the front collision warning FCW and the lane departure warning LDW. Exemplarily, in the pre-risk prediction category of the safety warning, if the warning function is triggered, it is determined to be a risk of the safety pre-trigger event type.

[0074] As mentioned above, five pre-risk prediction categories are defined, which can collect real vehicle data more efficiently and in a more targeted manner, and achieve richer data accumulation for potential deficiencies in autonomous driving functions.

[0075] 300. If the set condition is triggered, the alarm is activated, the buried data is recorded, and the buried data is uploaded to the cloud to instruct the cloud to perform risk analysis.

[0076] Understandably, the vehicle establishes a connection with the cloud through the on-board communication system (such as 4G / 5G, Wi-Fi, etc.) to ensure real-time transmission of data, so that the vehicle can upload its own status information, security log and other data to the cloud in real time for analysis and monitoring by the cloud.

[0077] Optionally, when there are abnormalities in the monitoring signals corresponding to different scenario categories, it is regarded as the triggering of the corresponding rules and the alarm is activated; thus, when conducting pre-risk prediction, once a possible safety hazard is discovered, an alarm is quickly given to the driver to enhance safety; at the same time, once an accident occurs, the on-board data is convenient for restoring the accident scene and clarifying the responsibilities of all parties.

[0078] In some embodiments, recording the tracking data and uploading the tracking data to the cloud to instruct the cloud to perform risk analysis includes:

[0079] Record buried data; the buried data includes: basic vehicle information, vehicle status and dynamic information, driver status and operation information, perception-related information, autonomous driving system operation information, instrument and HMI display information.

[0080] The data collection request is sent to the big data collection component through the vehicle-side trigger to trigger the upload of the vehicle's tracking data, instructing the cloud to complete scene restoration, problem analysis and risk assessment based on the tracking data, and send instructions for security risk repair and software upgrade.

[0081] Furthermore, the basic vehicle information may be the vehicle VIN, location, etc.; the vehicle status and dynamic information may be ignition information, etc.; the driver status and operation information may be driver status monitoring information, etc.; the perception-related information may be camera-related video, etc.; the automatic driving system operation information may be the requested steering angle, etc.; the instrument and HMI display information may be DTC-related information, etc.

[0082] As mentioned above, through the data recording and uploading functions, scenario data can be accumulated, R&D technical analysis can be supported, long-tail problem vulnerabilities can be solved, and product security can be improved.

[0083] 400. Receive and execute security risk repair instructions from the cloud to complete security monitoring.

[0084] Specifically, the vehicle receives security risk repair instructions sent from the cloud through the on-board communication system, where the security risk repair instructions may include operations such as updating software, fixing vulnerabilities, and adjusting security policies. After receiving the instructions, the vehicle will verify the instructions to ensure the legality and validity of the instructions.

[0085] Specifically, the vehicle parses the received security risk repair instructions to clarify the specific operation content and steps; according to the parsed instructions, the vehicle performs corresponding operations, such as updating software modules, repairing security vulnerabilities, adjusting security policies, etc., so as to avoid risks. It should be noted that these operations usually need to be performed under the premise of ensuring the normal operation of the vehicle to avoid affecting the normal use of the vehicle; optionally, after executing the security risk repair instructions, the vehicle will feedback the execution results to the cloud. The cloud will evaluate the execution effect of the instructions based on the feedback results and adjust the subsequent security policies accordingly.

[0086] As mentioned above, for example, the anti-lock braking system is activated for more than 1.5 seconds as a dangerous scenario: the pre-risk prediction safety monitoring vehicle-side trigger realizes pre-risk prediction by monitoring the L3 function activation status signal, the ABS function activation signal and the trigger's built-in timer. When the L3 function status signal and the ABS function status signal are both 1 (activated state), and the cumulative function activation time of the timer reaches 1.5s, the pre-risk prediction safety monitoring vehicle-side trigger will alarm the driver. At the same time, referring to the rule trigger timestamp identifier as the origin, the vehicle buried data within (-15s, 5s) is stored in the vehicle-side domain controller. When the collected data accumulates to a certain peak value, the trigger data is uploaded and stored in the cloud database. Pull the buried data in the database through the cloud platform to complete the scene restoration, risk assessment and problem analysis. After the safety risk is repaired, the vehicle software is upgraded through OTA.

[0087] As described above, the embodiment of the present application obtains vehicle status information; based on the vehicle status information, determines whether the result of the pre-risk prediction triggers the set conditions; if the set conditions are triggered, turns on the alarm, records the buried data, and uploads the buried data to the cloud to instruct the cloud to perform risk analysis; receives and executes security risk repair instructions from the cloud to complete safety monitoring; predicts possible risks in the autonomous driving process, detects existing safety hazards, and reduces the probability of safety accidents.

[0088] The embodiment of the present application defines five pre-risk prediction categories, which can achieve more efficient and targeted collection of real vehicle data, and achieve richer data accumulation for potential functional deficiencies of autonomous driving. The input data of the pre-risk prediction safety monitoring vehicle-side trigger covers more than 100 signals such as the body comfort domain, power domain, and intelligent driving domain. Through comprehensive analysis of them, it is possible to predict the risks that may exist during the driving process of the car and provide alarm information to the driver. Through real-time monitoring of pre-risk prediction safety monitoring, when the vehicle's driving status exceeds the safety boundary, the vehicle driving data is recorded in time and uploaded to the cloud.

[0089] The above steps are not to be performed in a strict order as described in the numbers, but should be understood as an overall solution.

[0090] In the second aspect, based on the above embodiments, Figure 2 A schematic diagram of the structure of a security monitoring device based on pre-risk prediction provided by an embodiment of the present application. Figure 2 The security monitoring device based on pre-risk prediction provided in this embodiment specifically includes: an information acquisition module 201, a trigger judgment module 202, an alarm feedback module 203 and a risk repair module 202.

[0091] Among them, the information acquisition module 201 is used to obtain vehicle status information; the trigger judgment module 202 is used to judge whether the result of the pre-risk prediction triggers the set conditions based on the vehicle status information; the alarm feedback module 203 is used to turn on the alarm if the set conditions are triggered, and record the buried data, and upload the buried data to the cloud to instruct the cloud to perform risk analysis; the risk repair module 204 is used to receive and execute security risk repair instructions from the cloud to complete security monitoring.

[0092] As described above, the embodiment of the present application obtains vehicle status information; based on the vehicle status information, determines whether the result of the pre-risk prediction triggers the set conditions; if the set conditions are triggered, turns on the alarm, records the buried data, and uploads the buried data to the cloud to instruct the cloud to perform risk analysis; receives and executes security risk repair instructions from the cloud to complete safety monitoring; predicts possible risks in the autonomous driving process, detects existing safety hazards, and reduces the probability of safety accidents.

[0093] The security monitoring device based on pre-risk prediction provided in the embodiment of the present application can be used to execute the security monitoring method based on pre-risk prediction provided in the above embodiment, and has corresponding functions and beneficial effects.

[0094] In a third aspect, an embodiment of the present application further provides an electronic device that can integrate the security monitoring device based on pre-risk prediction provided by an embodiment of the present application. Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3 The electronic device includes: an input device 43, an output device 44, a memory 42 and one or more processors 41; the memory 42 is used to store one or more programs; when the one or more programs are executed by the one or more processors 41, the one or more processors 41 implement the security monitoring method based on pre-risk prediction as provided in the above embodiment. The input device 43, the output device 44, the memory 42 and the processor 41 can be connected by a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0095] The processor 41 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 42, that is, implements the above-mentioned security monitoring method based on pre-risk prediction.

[0096] The electronic device provided above can be used to execute the security monitoring method based on pre-risk prediction provided in the above embodiment, and has corresponding functions and beneficial effects.

[0097] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the security monitoring method based on pre-risk prediction as described above, and can achieve the same beneficial effects.

[0098] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present application is not limited to the security monitoring method based on prior risk prediction as described above, and can also execute related operations in the security monitoring method based on prior risk prediction provided in any embodiment of the present application.

[0099] In a fifth aspect, the embodiments of the present application also provide a computer program product, and the methods described in the various embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the processes or functions described in the various embodiments of the present application are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM (OpenAplicationModel, open application model) or other programmable device.

[0100] The computer program or instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; it may also be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.

[0101] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0103] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling an electronic device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0104] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0106] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A security monitoring method based on pre-risk prediction, characterized in that: include: Get vehicle status information; According to the vehicle status information, determining whether the result of the pre-risk prediction triggers a set condition; If the set conditions are triggered, the alarm is activated, the tracking data is recorded, and the tracking data is uploaded to the cloud to instruct the cloud to perform risk analysis; Receive and execute security risk remediation instructions from the cloud to complete security monitoring.

2. The security monitoring method based on pre-risk prediction according to claim 1 is characterized in that: The vehicle status information includes: radar monitoring signals, various function status signals, various system fault signals, driver status safety signals, and four-door and two-lid status signals.

3. The security monitoring method based on pre-risk prediction according to claim 1 is characterized in that: The determining whether the result of the pre-risk prediction triggers the set condition includes: Determine whether the results of the pre-risk prediction trigger the set conditions, where the scenario categories include dangerous operation scenarios, abnormal vehicle operation, vehicle failure, driver misuse, and safety warnings.

4. The safety monitoring method based on pre-risk prediction according to claim 3 is characterized in that: The determining whether the result of the pre-risk prediction triggers the set condition includes: In the dangerous operation scenario category, the ESP vehicle stability control system, ABS anti-lock braking system and four-door and two-lid signal analysis are used to determine whether the vehicle is in a dangerous scenario; Among the types of vehicle operation abnormalities, determine whether the autonomous driving vehicle is abnormal by monitoring radar signals and autonomous driving status signals; Among the types of vehicle faults, it is determined whether the vehicle has a fault by monitoring the fault signals of the steering and braking systems; In the driver misuse category, the driver is judged whether there is dangerous or misuse driving behavior by monitoring the DMS system signal; wherein the DMS system signal includes the seat buckle signal and the fatigue alarm signal; Among the safety warning types, the front collision warning FCW activation signal and lane departure warning LDW activation signal are monitored to predict whether the vehicle has a collision safety risk.

5. The security monitoring method based on pre-risk prediction according to claim 4 is characterized in that: If the set condition is triggered, the alarm is activated, including: When there are abnormalities in the monitoring signals corresponding to different scene categories, it is regarded as the triggering of the corresponding rules and the alarm is activated.

6. The security monitoring method based on pre-risk prediction according to claim 1 is characterized in that: The recording of the tracking data and uploading the tracking data to the cloud to instruct the cloud to perform risk analysis include: Record buried data; The vehicle-side trigger sends the data collection request to the big data collection component to trigger the upload of the vehicle's tracking data, instruct the cloud to complete scene restoration, problem analysis and risk assessment based on the tracking data, and send instructions for security risk repair and software upgrade; Among them, the buried data includes: basic vehicle information, vehicle status and dynamic information, driver status and operation information, perception-related information, automatic driving system operation information, instrument and HMI display information.

7. A safety monitoring device based on pre-risk prediction, characterized in that: include: An information acquisition module, used to acquire vehicle status information; A trigger judgment module, used to judge whether the result of the pre-risk prediction triggers a set condition according to the vehicle status information; The alarm feedback module is used to activate the alarm if the set conditions are triggered, record the buried point data, and upload the buried point data to the cloud to instruct the cloud to perform risk analysis; The risk repair module is used to receive and execute security risk repair instructions from the cloud to complete security monitoring.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the processor is connected to the memory via the bus, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, they are used to implement the security monitoring method based on pre-risk prediction as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a server, implements a security monitoring method based on pre-risk prediction as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises instructions, which, when executed by a computer, enable the computer to implement the security monitoring method based on pre-risk prediction according to any one of claims 1 to 6.

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